Instructions to use build-small-hackathon/facade-of-jade-qwen3-4b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use build-small-hackathon/facade-of-jade-qwen3-4b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "build-small-hackathon/facade-of-jade-qwen3-4b-lora") - Notebooks
- Google Colab
- Kaggle
metadata
base_model: Qwen/Qwen3-4B-Instruct-2507
library_name: peft
tags:
- lora
- qwen3
- build-small-hackathon
- facade-of-jade
- modal
Facade of Jade Qwen3-4B LoRA
LoRA adapter trained for Facade of Jade, a Build Small Hackathon interactive wuxia drama demo.
- Base model:
Qwen/Qwen3-4B-Instruct-2507 - Training records: 50
- Epochs: 3
- Final train loss:
2.969015 - Adapter size reported by Modal runner:
483.63 MB - Modal run evidence: https://modal.com/apps/t-abdullah-rashid/main/ap-W54lCMfJu4eu3UCVQvVpQK
- Source repo: https://github.com/tuancookiez-hub/facade-of-jade
- Live Space: https://build-small-hackathon-facade-of-jade.hf.space
This adapter was produced by train_lora_modal.py on Modal A100-80GB and saved from Modal volume facade-of-jade-lora-out.